With the advancement in communication technology and processing capabilities of computing devices, a large number of users are accessing content and data from various online content sources such as, but not limited to, file transferring portals, online data repositories, search engines, meta search portals, online archives, online encyclopedias, and the like. Usually online content sources may track user's browsing behavior to determine the type of content that the user may be of interest to the user. Thereafter, the online content sources may recommend content to the user based on the determined browsing behavior of the user.
In certain scenarios, the users may have privacy and confidentiality concerns regarding the tracking of their browsing behavior. A user may not want that the user's sensitive information be sent to an online content source when the online content source tracks the user's browsing behavior. Further, the online content source may be able to ascertain certain user specific information by analyzing the content recommended to the user by the online content source. This may also lead to compromising the privacy and confidentiality of the user. Hence, there is a need for a solution for enabling the online content sources to recommend content to the users in such a manner that the privacy and confidentiality of the users is not compromised.